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Deep Learning Corpus Callosum Segmentation as a Neurodegenerative Marker in Multiple Sclerosis
Author(s) -
Platten Michael,
Brusini Irene,
Andersson Olle,
Ouellette Russell,
Piehl Fredrik,
Wang Chunliang,
Granberg Tobias
Publication year - 2021
Publication title -
journal of neuroimaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.822
H-Index - 64
eISSN - 1552-6569
pISSN - 1051-2284
DOI - 10.1111/jon.12838
Subject(s) - corpus callosum , medicine , expanded disability status scale , multiple sclerosis , atrophy , segmentation , magnetic resonance imaging , physical medicine and rehabilitation , pathology , artificial intelligence , radiology , computer science , psychiatry
Corpus callosum atrophy is a sensitive biomarker of multiple sclerosis (MS) neurodegeneration but typically requires manual 2D or volumetric 3D-based segmentations. We developed a supervised machine learning algorithm, DeepnCCA, for corpus callosum segmentation and relate callosal morphology to clinical disability using conventional MRI scans collected in clinical routine.